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TinyCD/distill/best_mIoU_iter_69000.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c2f86011537aa77f3e053c993b35bf8220e3a8c09819545122babd21f2b79df9
+size 14166085

TinyCD/distill/config.py

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+_base_ = [
+    '/home/liuziyuan/proj/rmcd-kd/configs/_base_/models/KD-tinycd.py', 
+    '/home/liuziyuan/proj/rmcd-kd/configs/common/standard_512x512_200k_cgwx.py']
+
+dataset_type = 'LEVIR_CD_Dataset'
+data_root = '/nas/datasets/lzy/RS-ChangeDetection/CGWX'
+
+crop_size = (512, 512)
+
+checkpoint_student = '/nas/datasets/lzy/RS-ChangeDetection/Best_ckpt_3/TinyCD/initial/best_mIoU_iter_85000.pth'
+checkpoint_teacher_l = '/nas/datasets/lzy/RS-ChangeDetection/Best_ckpt_3/TinyCD/large/best_mIoU_iter_102500.pth'
+checkpoint_teacher_m = '/nas/datasets/lzy/RS-ChangeDetection/Best_ckpt_3/TinyCD/medium/best_mIoU_iter_49000.pth'
+checkpoint_teacher_s = '/nas/datasets/lzy/RS-ChangeDetection/Best_ckpt_3/TinyCD/small/best_mIoU_iter_192000.pth'
+
+model = dict(
+    # student
+    init_cfg=dict(type='Pretrained', checkpoint=checkpoint_student),
+    # teacher large    
+    init_cfg_t_l = dict(type='Pretrained', checkpoint=checkpoint_teacher_l),
+    # teacher medium    
+    init_cfg_t_m = dict(type='Pretrained', checkpoint=checkpoint_teacher_m),
+    # teacher small    
+    init_cfg_t_s = dict(type='Pretrained', checkpoint=checkpoint_teacher_s),
+    
+    decode_head=dict(num_classes=2, out_channels=1),
+    # test_cfg=dict(mode='slide', crop_size=crop_size, stride=(crop_size[0]//2, crop_size[1]//2))
+)
+
+# optimizer
+optimizer = dict(
+    type='AdamW',
+    lr=0.00356799066427741,
+    betas=(0.9, 0.999),
+    weight_decay=0.009449677083344786)
+
+optim_wrapper = dict(
+    _delete_=True,
+    type='OptimWrapper',
+    optimizer=optimizer)
+
+param_scheduler = [
+    dict(
+        type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1000),
+    dict(
+        type='PolyLR',
+        power=1.0,
+        begin=1000,
+        end=100000,
+        eta_min=0.0,
+        by_epoch=False,
+    )
+]
+
+# training schedule for 100k
+train_cfg = dict(type='IterBasedTrainLoop', max_iters=100000, val_interval=1000)
+val_cfg = dict(type='ValLoop')
+test_cfg = dict(type='TestLoop')
+default_hooks = dict(
+    timer=dict(type='IterTimerHook'),
+    logger=dict(type='LoggerHook', interval=100, log_metric_by_epoch=False),
+    param_scheduler=dict(type='ParamSchedulerHook'),
+    checkpoint=dict(type='CheckpointHook', by_epoch=False, interval=1000,
+                    save_best='mIoU'),
+    sampler_seed=dict(type='DistSamplerSeedHook'),
+    visualization=dict(type='CDVisualizationHook', interval=1, 
+                       img_shape=(512, 512, 3)))

TinyCD/initial/best_mIoU_iter_85000.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:56ac5bcf828407ec1d99ef3c97ecd5b44e1867ff7ebe5656744b5a1e3cb9a163
+size 10677335

TinyCD/initial/config.py

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+_base_ = [
+    '/home/liuziyuan/proj/RS-CD/rs-cd-cgwx/configs/_base_/models/tinycd.py', 
+    '/home/liuziyuan/proj/RS-CD/rs-cd-cgwx/configs/common/standard_512x512_200k_cgwx.py']
+
+dataset_type = 'LEVIR_CD_Dataset'
+data_root = '/nas/datasets/lzy/RS-ChangeDetection/CGWX'
+
+crop_size = (512, 512)
+model = dict(
+    decode_head=dict(num_classes=2, out_channels=1),
+    # test_cfg=dict(mode='slide', crop_size=crop_size, stride=(crop_size[0]//2, crop_size[1]//2))
+)
+
+# optimizer
+optimizer = dict(
+    type='AdamW',
+    lr=0.00356799066427741,
+    betas=(0.9, 0.999),
+    weight_decay=0.009449677083344786)
+
+optim_wrapper = dict(
+    _delete_=True,
+    type='OptimWrapper',
+    optimizer=optimizer)

TinyCD/large/best_mIoU_iter_102500.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4817627805a03792e7d7fc3bc386fb0df2a3c8d3a0e48c80a5ad2690d54366df
+size 12582231

TinyCD/large/config.py

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+_base_ = [
+    '/home/liuziyuan/proj/RS-CD/rs-cd-cgwx/configs/_base_/models/tinycd.py', 
+    '/home/liuziyuan/proj/RS-CD/rs-cd-cgwx/configs/common/train_large_512x512_100k_cgwx.py']
+
+dataset_type = 'LEVIR_CD_Dataset'
+data_root = '/nas/datasets/lzy/RS-ChangeDetection/CGWX'
+
+crop_size = (512, 512)
+model = dict(
+    decode_head=dict(num_classes=2, out_channels=1),
+    # test_cfg=dict(mode='slide', crop_size=crop_size, stride=(crop_size[0]//2, crop_size[1]//2))
+)
+
+
+# optimizer
+optimizer = dict(
+    type='AdamW',
+    lr=0.00356799066427741,
+    betas=(0.9, 0.999),
+    weight_decay=0.009449677083344786)
+
+optim_wrapper = dict(
+    _delete_=True,
+    type='OptimWrapper',
+    optimizer=optimizer)
+
+param_scheduler = [
+    dict(
+        type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1000),
+    dict(
+        type='PolyLR',
+        power=1.0,
+        begin=1000,
+        end=200000,
+        eta_min=0.0,
+        by_epoch=False,
+    )
+]
+# training schedule for 100k
+train_cfg = dict(type='IterBasedTrainLoop', max_iters=200000, val_interval=1000)
+
+default_hooks = dict(
+    timer=dict(type='IterTimerHook'),
+    logger=dict(type='LoggerHook', interval=100, log_metric_by_epoch=False),
+    param_scheduler=dict(type='ParamSchedulerHook'),
+    checkpoint=dict(type='CheckpointHook', by_epoch=False, interval=1000,
+                    save_best='mIoU'),
+    sampler_seed=dict(type='DistSamplerSeedHook'),
+    visualization=dict(type='CDVisualizationHook', interval=1, 
+                       img_shape=(512, 512, 3)))

TinyCD/medium/best_mIoU_iter_49000.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:090097ef95cc5d49ec682d1b9a3164262c165d721895a6d08cf2307da8cea769
+size 6703959

TinyCD/medium/config.py

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+_base_ = [
+    '/home/liuziyuan/proj/RS-CD/rs-cd-cgwx/configs/_base_/models/tinycd.py', 
+    '/home/liuziyuan/proj/RS-CD/rs-cd-cgwx/configs/common/train_medium_512x512_100k_cgwx.py']
+
+dataset_type = 'LEVIR_CD_Dataset'
+data_root = '/nas/datasets/lzy/RS-ChangeDetection/CGWX'
+
+crop_size = (512, 512)
+model = dict(
+    decode_head=dict(num_classes=2, out_channels=1),
+    # test_cfg=dict(mode='slide', crop_size=crop_size, stride=(crop_size[0]//2, crop_size[1]//2))
+)
+
+# optimizer
+optimizer = dict(
+    type='AdamW',
+    lr=0.00356799066427741,
+    betas=(0.9, 0.999),
+    weight_decay=0.009449677083344786)
+
+optim_wrapper = dict(
+    _delete_=True,
+    type='OptimWrapper',
+    optimizer=optimizer)
+
+param_scheduler = [
+    dict(
+        type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1000),
+    dict(
+        type='PolyLR',
+        power=1.0,
+        begin=1000,
+        end=200000,
+        eta_min=0.0,
+        by_epoch=False,
+    )
+]
+# training schedule for 100k
+train_cfg = dict(type='IterBasedTrainLoop', max_iters=200000, val_interval=1000)
+
+default_hooks = dict(
+    timer=dict(type='IterTimerHook'),
+    logger=dict(type='LoggerHook', interval=100, log_metric_by_epoch=False),
+    param_scheduler=dict(type='ParamSchedulerHook'),
+    checkpoint=dict(type='CheckpointHook', by_epoch=False, interval=1000,
+                    save_best='mIoU'),
+    sampler_seed=dict(type='DistSamplerSeedHook'),
+    visualization=dict(type='CDVisualizationHook', interval=1, 
+                       img_shape=(512, 512, 3)))

TinyCD/small/best_mIoU_iter_192000.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:285adf3558854f79ba32e2e1ce18309a86240189d713ee535f554038f22628d1
+size 22718999

TinyCD/small/config.py

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+_base_ = [
+    '/home/liuziyuan/proj/RS-CD/rs-cd-cgwx/configs/_base_/models/tinycd.py', 
+    '/home/liuziyuan/proj/RS-CD/rs-cd-cgwx/configs/common/train_small_512x512_100k_cgwx.py']
+
+dataset_type = 'LEVIR_CD_Dataset'
+data_root = '/nas/datasets/lzy/RS-ChangeDetection/CGWX'
+
+crop_size = (512, 512)
+model = dict(
+    decode_head=dict(num_classes=2, out_channels=1),
+    # test_cfg=dict(mode='slide', crop_size=crop_size, stride=(crop_size[0]//2, crop_size[1]//2))
+)
+
+# optimizer
+optimizer = dict(
+    type='AdamW',
+    lr=0.00356799066427741,
+    betas=(0.9, 0.999),
+    weight_decay=0.009449677083344786)
+
+optim_wrapper = dict(
+    _delete_=True,
+    type='OptimWrapper',
+    optimizer=optimizer)
+
+param_scheduler = [
+    dict(
+        type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1000),
+    dict(
+        type='PolyLR',
+        power=1.0,
+        begin=1000,
+        end=200000,
+        eta_min=0.0,
+        by_epoch=False,
+    )
+]
+# training schedule for 100k
+train_cfg = dict(type='IterBasedTrainLoop', max_iters=200000, val_interval=1000)
+
+default_hooks = dict(
+    timer=dict(type='IterTimerHook'),
+    logger=dict(type='LoggerHook', interval=100, log_metric_by_epoch=False),
+    param_scheduler=dict(type='ParamSchedulerHook'),
+    checkpoint=dict(type='CheckpointHook', by_epoch=False, interval=1000,
+                    save_best='mIoU'),
+    sampler_seed=dict(type='DistSamplerSeedHook'),
+    visualization=dict(type='CDVisualizationHook', interval=1, 
+                       img_shape=(512, 512, 3)))